Multi-view clustering via canonical correlation analysis K Chaudhuri, SM Kakade, K Livescu, K Sridharan Proceedings of the 26th annual international conference on machine learning …, 2009 | 695 | 2009 |
Making gradient descent optimal for strongly convex stochastic optimization A Rakhlin, O Shamir, K Sridharan arXiv preprint arXiv:1109.5647, 2011 | 482 | 2011 |
Learnability, stability and uniform convergence S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan The Journal of Machine Learning Research 11, 2635-2670, 2010 | 288 | 2010 |
On the complexity of linear prediction: Risk bounds, margin bounds, and regularization SM Kakade, K Sridharan, A Tewari | 286 | 2008 |
Better mini-batch algorithms via accelerated gradient methods A Cotter, O Shamir, N Srebro, K Sridharan arXiv preprint arXiv:1106.4574, 2011 | 268 | 2011 |
Stochastic Convex Optimization. S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan COLT, 2009 | 235 | 2009 |
Smoothness, low noise and fast rates N Srebro, K Sridharan, A Tewari Advances in neural information processing systems 23, 2010 | 216* | 2010 |
Online learning with predictable sequences A Rakhlin, K Sridharan Conference on Learning Theory, 993-1019, 2013 | 167 | 2013 |
Optimization, learning, and games with predictable sequences A Rakhlin, K Sridharan arXiv preprint arXiv:1311.1869, 2013 | 162 | 2013 |
Fast rates for regularized objectives K Sridharan, S Shalev-Shwartz, N Srebro Advances in neural information processing systems 21, 1545-1552, 2008 | 134 | 2008 |
Online optimization: Competing with dynamic comparators A Jadbabaie, A Rakhlin, S Shahrampour, K Sridharan Artificial Intelligence and Statistics, 398-406, 2015 | 130 | 2015 |
On the universality of online mirror descent N Srebro, K Sridharan, A Tewari arXiv preprint arXiv:1107.4080, 2011 | 106 | 2011 |
Online learning: Random averages, combinatorial parameters, and learnability A Rakhlin, K Sridharan, A Tewari | 93 | 2010 |
Selective sampling and active learning from single and multiple teachers O Dekel, C Gentile, K Sridharan The Journal of Machine Learning Research 13 (1), 2655-2697, 2012 | 86 | 2012 |
Learning kernel-based halfspaces with the 0-1 loss S Shalev-Shwartz, O Shamir, K Sridharan SIAM Journal on Computing 40 (6), 1623-1646, 2011 | 83* | 2011 |
An information theoretic framework for multi-view learning K Sridharan, SM Kakade | 82 | 2008 |
Online learning: Beyond regret A Rakhlin, K Sridharan, A Tewari Proceedings of the 24th Annual Conference on Learning Theory, 559-594, 2011 | 74 | 2011 |
Relax and randomize: From value to algorithms A Rakhlin, O Shamir, K Sridharan | 71* | 2012 |
Learning exponential families in high-dimensions: Strong convexity and sparsity S Kakade, O Shamir, K Sindharan, A Tewari Proceedings of the thirteenth international conference on artificial …, 2010 | 69 | 2010 |
Theory of deep learning IIb: Optimization properties of SGD C Zhang, Q Liao, A Rakhlin, B Miranda, N Golowich, T Poggio arXiv preprint arXiv:1801.02254, 2018 | 67* | 2018 |